A large amount of a data scientist's precious time is spent on data preparation, merging of files from a variety of sources, dealing with inconsistencies, transforming raw data and so on. Organizations are looking toward machine learning to tackle the task that traditional processes struggle with; that is, to deliver valuable intelligence from the vast amounts of data. Organizations should also consider the newer hardware capabilities such as embedded hardware systems to handle machine learning, computer vision and deep learning faster than traditional systems. Considerations toward SaaS apps, integration of data and intelligent applications should also be considered as organizations transition to a multicloud environment.
Organizations are learning to monetize their information by using advanced analytics techniques and processes, and data is emerging as a valued form of "currency" at the core. Most organizations positioning themselves to benefit from this new data economy need to adhere to an architecture governed by a central architecture board, drive collaboration, break down silos and integrate data, and ensure timely access to information across the multicloud environment.
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